Speaker: Jose Zubizaretta, Harvard Medical School, "Bridging Matching, Regression, and Weighting as Mathematical Programs for Causal Inference"

Abstract: A fundamental principle in the design of observational studies is to approximate the randomized experiment that would have been conducted under controlled circumstances. Across the health and social sciences, statistical methods for covariate adjustment are used in pursuit of this principle. Basic methods are matching, regression, and weighting. In this talk, we will examine the connections between these methods through their underlying mathematical programs. We will study their strengths and weaknesses in terms of study design, computational tractability, and statistical efficiency. Overall, we will discuss the role of mathematical optimization for the design and analysis of studies of causal effects.

About the speaker: José Zubizarreta, PhD, is an associate professor in the Department of Health Care Policy at Harvard Medical School, an associate professor in the Department of Biostatistics at Harvard School of Public Health, and a faculty affiliate in the Department of Statistics at the Faculty of Arts and Sciences at Harvard University.  His work centers on the development of statistical methods for causal inference and impact evaluation to advance research in health care and public policy.  His research has been supported by the Alfred P. Sloan Foundation.  He is a Fellow of the American Statistical Association, and is a recipient of the Kenneth Rothman Award, the William Cochran Award, and the Tom Ten Have Memorial Award.